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135 results for “Arctic Region”
Fish captures in lakes of the Arctic LTER region Toolik Field Station Alaska from 1986 to 2021.
This file contains the fish number, recap number, species, lengths, weights, sex and a list of tissues sampled of fish captured in lakes near the Toolik Lake Arctic LTER site during summers from 1986 to 2021. The file also contains information from gill-netted fish (if any), sacrificed fish, and accidentally killed fish. All dead fish are included, and if their stomachs and otoliths were taken, that is noted also.
Supporting Data for: McKenna et al. (2018), Arctic sea-ice loss in different regions leads to contrasting Northern Hemisphere impacts
<p>This is a dataset of output from version 4 of the Reading Intermediate Global Circulation Model (IGCM4) that was used in the article: </p> <p>McKenna, C. M., Bracegirdle, T. J., Shuckburgh, E. F., Haynes, P. H., & Joshi, M. M. (2018). Arctic sea ice loss in different regions leads to contrasting Northern Hemisphere impacts. <em>Geophysical Research Letters</em>, 45, 945-954. <a href="https://doi.org/10.1002/2017GL076433">https://doi.org/10.1002/2017GL076433</a></p> <p> </p> <p>Files required to setup the IGCM4 simulations are given in the directory 'IGCM4_setup'.</p> <p>All other directories contain netcdf files of timeseries of various monthly mean fields for each IGCM4 simulation (see paper for details on these simulations). The available variables are:</p> <ul> <li>ua: zonal winds</li> <li>zg: geopotential height</li> <li>ts: surface temperature</li> <li>hfls, hfss, rlds, rlus: surface heatfluxes</li> <li>Flat, Fz, divF: Eliassen-Palm flux vectors and their divergence (only for months November-February)</li> </ul> <p>The ua and zg variables are given for different pressure levels indicated in the filenames (e.g., ua500 is ua at 500 hPa). ua is additionally given in terms of the zonal mean with latitude and pressure. zg is additionally given in terms of longitude and pressure, averaged over latitudes between 60N-80N. All files follow CF conventions in terms of metadata, variable names, etc. </p> <p>Note that the CTL, ATL, PAC, and ATLandPAC simulations were all run continuously in time (i.e., every year starts from the end of the previous year). The 0.5ATL and 0.5PAC simulations, however, were run for 300 years in three separate 100-year chunks (i.e., the initial conditions used to start each 100-year chunk were different). The three 100-year chunks have been appended together in the netcdf files. </p>
Arctic vegetation cover fractions derived from Landsat time series (1984-2020) for the greater Mackenzie Delta Region (Western Canadian Arctic)
<p>Data to the publication by Nill et al. (2022) "<em>Arctic shrub expansion revealed by Landsat-derived multitemporal<br> vegetation cover fractions in the Western Canadian Arctic"</em></p> <p>The dataset features Landsat-derived fractional cover estimates of Arctic plant functional types (shrub, evergreen trees, herbaceous, lichen) and other land cover (barren, water) in the greater Mackenzie Delta Region, Canada.<br> We utilized regression-based unmixing based on synthetic training data in order to build multitemporal Kernel Ridge Regression (KRR) models for estimating fractional cover and validated our predictions based on independent very-high-resolution imagery (please be referred to publication for details).<br> <br> <strong>Dataset information</strong><br> The fraction cover predictions ("krr-avg") are provided separately for each epoch (1984-1990, 1991-1996, ..., 2015-2020) and class/cover type. The decadal change images ("dec-cng") between 1984 and 2020 are provided separately for each class/cover type. The naming convention of the files is as follows:</p> <p>XXXX-XXXX_YYY-YYY_int16-10e3_class-Z-Z</p> <ul> <li>XXXX-XXXX = epoch, e.g. 2015-2020</li> <li>YYY-YYY = dataset ("krr-avg" = fraction cover, "dec-cng" = decadal fraction cover change)</li> <li>Z-Z = class ID and associated class name (sh = shrub, cf = coniferous, hb = herbaceous, lc = lichen, wt = water, br = barren)</li> </ul> <p>The fraction cover values are % scaled by 10,000. For instance, a value of 1234 refers to 12.34%. Further image metadata:</p> <ul> <li><strong>Datatype:</strong> Signed 16-bit integer (Int16) </li> <li><strong>Data format: </strong>GeoTiff (.tif)</li> <li><strong>No data value:</strong> -9999</li> <li><strong>Projection:</strong> EPSG:3573 with custom central meridian; WKT string: 'PROJCS["WGS 84 / North Pole LAEA Canada",GEOGCS["WGS 84",DATUM["WGS_1984",SPHEROID["WGS 84",6378137,298.257223563,AUTHORITY["EPSG","7030"]],AUTHORITY["EPSG","6326"]],PRIMEM["Greenwich",0],UNIT["degree",0.0174532925199433,AUTHORITY["EPSG","9122"]],AUTHORITY["EPSG","4326"]],PROJECTION["Lambert_Azimuthal_Equal_Area"],PARAMETER["latitude_of_center",90],PARAMETER["longitude_of_center",-135],PARAMETER["false_easting",0],PARAMETER["false_northing",0],UNIT["metre",1],AXIS["Easting",EAST],AXIS["Northing",NORTH]]'</li> </ul> <p><strong>Publication</strong><br> Nill, L., Grünberg, I., Ullmann, T., Gessner, M., Boike, J. & Hostert, P. (2022): Arctic shrub expansion revealed by Landsat-derived multitemporal vegetation cover fractions in the Western Canadian Arctic. Remote Sensing of Environment, 2022, 281. https://doi.org/10.1016/j.rse.2022.113228</p> <p><strong>Further information</strong><br> For further information, please see the publication or contact Leon Nill (leon.nill@geo.hu-berlin.de).<br> A web-visualization of this dataset is available <a href="https://ows.geo.hu-berlin.de/webviewer/arctic-shrub/">here</a>.</p>
Data used in "Quiet night Arctic ionospheric D region characteristics"
<p>Data used in 'Quiet night Arctic ionospheric D region characteristics', as zipped text files</p>
The Coastal Streamflow Flux in the Regional Arctic System Model
<p>The Arctic coastal streamflow flux is an important driver of dynamics in the coupled ice-ocean system. We have developed a new streamflow routing model (RVIC), coupled within the Regional Arctic System Model (RASM), to simulate the coastal freshwater flux. RASM is a high-resolution regional Earth system model applied over a Pan-Arctic model domain. This dataset includes distributed daily coastal streamflows between 1979 and 2014 for the RASM domain. In Hamman et al. (2017) we demonstrate the performance of RASM and RVIC-simulated streamflow in fully coupled model simulations and discuss the improvements this derived dataset has, relative to existing distributed datasets in the Arctic.</p> <p>See the following references for further details on this dataset:</p> <p>Hamman, J., B. Nijssen, A. Roberts, A. Craig, W. Maslowski, and R. Osinski, 2017: The Coastal Streamflow Flux in the Regional Arctic System Model. Journal of Geophysical Research: Oceans, doi:10.1002/2016JC012323.</p> <p>Hamman, J., B. Nijssen, M. Brunke, J. Cassano, A. Craig, A. DuVivier, M. Hughes, D.P. Lettenmaier, W. Maslowski, R. Osinski, A. Roberts, and X. Zeng, 2016: Land surface climate in the Regional Arctic System Model. Journal of Climate, doi:10.1175/JCLI-D-15-0415.1.</p>
Figure 12 in Towards a revision of the genus Halectinosoma (Copepoda, Harpacticoida, Ectinosomatidae): new species from the North Atlantic and Arctic regions
Figure 12. Halectinosoma kliei sp. nov. Female holotype: A, antennule (setae omitted); B, antenna; C, coxal gnathobase of mandible; D, praecoxal arthrite of maxillula; E, maxilla; F, maxilliped. Male paratype: G, antennule (setae omitted).
Figure 11 in Towards a revision of the genus Halectinosoma (Copepoda, Harpacticoida, Ectinosomatidae): new species from the North Atlantic and Arctic regions
Figure 11. Halectinosoma kliei sp. nov. Female holotype: A, habitus, dorsal; B, urosomites 2–6, ventral; C, urosomites 2–6, dorsal; D, P5. Male paratype: E, P5; F, P6.
Figure 10 in Towards a revision of the genus Halectinosoma (Copepoda, Harpacticoida, Ectinosomatidae): new species from the North Atlantic and Arctic regions
Figure 10. Halectinosoma paragothiceps sp. nov. Female (South Queensferry): A, P1; B, P2; C, P3; D, P4.
Figure 9 in Towards a revision of the genus Halectinosoma (Copepoda, Harpacticoida, Ectinosomatidae): new species from the North Atlantic and Arctic regions
Figure 9. Halectinosoma paragothiceps sp. nov. Female (South Queensferry): A, urosomites 2–6, ventral; B, urosomites 2–6, dorsal. H. gothiceps (Giesbrecht, 1881). Female (South Queensferry): C, urosomites 2–6, ventral; D, urosomites 2–6, dorsal.
Figure 8 in Towards a revision of the genus Halectinosoma (Copepoda, Harpacticoida, Ectinosomatidae): new species from the North Atlantic and Arctic regions
Figure 8. Halectinosoma paragothiceps sp. nov. Female holotype: A, antenna; B, mandible; C, maxillula; D, maxilla; E, maxilliped.
Figure 5 in Towards a revision of the genus Halectinosoma (Copepoda, Harpacticoida, Ectinosomatidae): new species from the North Atlantic and Arctic regions
Figure 5. Halectinosoma latisetifera sp. nov. Female: A, antennule (holotype); B, antenna (holotype); C, mandible (paratype NHM1990.422); D, maxillula (paratype NHM1990.423); E, maxilla (holotype); F, maxilliped (paratype NHM1990.423).
Figure 7 in Towards a revision of the genus Halectinosoma (Copepoda, Harpacticoida, Ectinosomatidae): new species from the North Atlantic and Arctic regions
Figure 7. Halectinosoma paragothiceps sp. nov. Female holotype: A, habitus, dorsal; B, labrum; C, P5. H. gothiceps (Giesbrecht, 1881). Female (South Queensferry): D, P5; E, anterior cephalothorax, lateral. H. gothiceps (Giesbrecht, 1881). Male (S21): F, P6. H. paragothiceps sp. nov. Male paratype NHM2005.2639 (S96): G, P5; H, P6.
Figure 4 in Towards a revision of the genus Halectinosoma (Copepoda, Harpacticoida, Ectinosomatidae): new species from the North Atlantic and Arctic regions
Figure 4. Halectinosoma latisetifera sp. nov. Female holotype: A, habitus, dorsal; B, labrum; C, urosomites 2–6, ventral; D, urosomites 2–6, dorsal; E, P5.
Figure 2 in Towards a revision of the genus Halectinosoma (Copepoda, Harpacticoida, Ectinosomatidae): new species from the North Atlantic and Arctic regions
Figure 2. Halectinosoma mandibularis sp. nov. Female holotype: A, antennule; B, antenna; C, mandible; D, maxillula; E, maxilla; F, maxilliped.
Figure 1 in Towards a revision of the genus Halectinosoma (Copepoda, Harpacticoida, Ectinosomatidae): new species from the North Atlantic and Arctic regions
Figure 1. Halectinosoma mandibularis sp. nov. Female holotype: A, habitus, dorsal; B, urosomites 2–6, ventral; C, urosomites 2–6, dorsal; D, P5. Male paratype (NHM1990.427): E, antennule; F, P5; G, P6.
Virtual stations (TeroVIR ) and water level time series (TeroWAT) in West Africa and Arctic regions
<p>The dataset contains a sample of locations across Siberia and Africa, for which water-level time series were automatically derived from Sentinel-3 altimeters (methodology described in Machefer et al. 2022<sup>1</sup>) from year 2016 to year 2021, together with the in-situ station records and the area covered by the altimetry measurements. The purpose of this dataset is validation and exemplification of the methodology. </p> <p>The methodology described produces comprehensive water level records at a global scale based on altimetry satellite data. The validation against in-situ data was assessed in numerous environments in West Africa and complex locations such as Arctic rivers partially covered with ice.<br> <br> This dataset offers a sample of the records at 3 locations in West Africa (Kemacina [Mali], Koulikouro [Mali], Lokoja [Niger]) and in the sub-arctic region (Yakutsk [Russia]). The data are organised by Level 1 of <a href="http://www.hydrosheds.org/">HydroBASINS</a><sup>2 </sup>definition (ex: africa) in two folders, each containing: virtual stations (teroVIR) and insitu stations (insitu) as shapefiles with their associated metadata, the corresponding water level time series (teroWAT) in NetCDF, and the level 3 of HydroBASINS, corresponding to the largest river basins of each continent. Finally, a csv file (validation) presents the computed metrics assessing the accuracy of the processors.</p> <p>N.B.: time series with less than two common date points between insitu and teroWAT have not been assessed. </p> <p>[1] Machefer, M., Perpinyà-Vallès M., Escorihuela M.J., Gustafsson D., Romero L. (2022): Challenges and evolution of water level monitoring towards a comprehensive, world-scale coverage with remote sensing. Earth System Science Data (Under Reviewing)</p> <p>[2] Lehner, B., Grill G. (2013): Global river hydrography and network routing: baseline data and new approaches to study the world’s large river systems. Hydrological Processes, 27(15): 2171–2186. Data is available at www.hydrosheds.org.</p>
Fig. 6 in Evidence Supporting the Concept of a Regionalized Distribution of Testate Amoebae in the Arctic
Fig. 6. Distribution map of Centropyxis pontigulasiformis and C. gasparella in the Arctic and subarctic Norway. Remark that C. pontigulasiformis has recently been observed in the Netherlands and in Austria.
Fig. 4 in Evidence Supporting the Concept of a Regionalized Distribution of Testate Amoebae in the Arctic
Fig. 4. Map of the Arctic (polar view) with climatic zonations according to Prik (1981) and the location of the different clustergroup members. The delination of the southern limit of the Arctic is given by the arrow with the number 1. The arrow 2 gives the southern limit of the High Arctic, arrow 3 the southern limit of the Polar Desert zone (according to Aleksandrova 1980). Sv-Gr stands for the suggested SvalbardGreenlandic protozoological region, Si stands for the Siberian.
LAND RESOURCES OF YAKUTIA'S AGRICULTURE IN THE LAST DECADE OF SOCIALISM: PECULIARITIES OF LAND ACCOUNTING OF STATE FARMS IN THE ARCTIC AND NORTHERN REGIONS
<p><span>The article shows the peculiarities of land resources utilization in the traditional economy of the indigenous population of Yakutia in the last decade of the Soviet period with a separate delineation of the state of the land balance and lands used in agriculture in 1990-1991. Including on the basis of archival data on land resources of state farms of the studied 15 arctic and northern regions, peculiarities of their accounting, preliminary results of statistical analysis of land resources of these large farms are obtained. The author introduces into scientific turnover new factual materials on land resources of separate state farms for the last Soviet 1991, in particular on their agricultural lands, reindeer and horse pastures.</span></p>
Elucidating the present-day chemical composition, seasonality and source regions of climate-relevant aerosols across the Arctic land surface
<p>Data presented in the figures of the journal article "Elucidating the present-day chemical composition, seasonality and source regions of climate-relevant aerosols across the Arctic land surface" by Moschos et al.</p>
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